PROJECT SUMMARY Pediatric severe obesity (defined as body mass index (BMI) ?1.2 times the 95th percentile or BMI ?35 kg/m2) is the fastest growing obesity category, affecting ~6% of youth in the U.S. Youth with severe obesity are at significant risk for developing obesity-related health consequences including type 2 diabetes and cardiovascular disease. Obesity pharmacotherapy is a promising adjunct to lifestyle modification (LSM) therapy for severe pediatric obesity treatment, as LSM alone generally fails to result in clinically significant and durable weight loss and metabolic/bariatric surgery is invasive, carries surgical risks, and is not widely available. While obesity pharmacotherapies are associated with overall mean weight loss, there is substantial variability in their individual- level effectiveness. The National Institutes of Health (NIH) has recognized the importance of identifying phenotypic characteristics associated with medication responsiveness, using pharmacogenomics approaches to develop precision pharmacologic treatments, and optimizing medication dosing based upon a person?s characteristics, in order to improve treatment of pediatric severe obesity. The objective of this proposal is to utilize techniques that can be used in precision medicine to identify person-specific characteristics associated with weight loss response to and affecting dosing of obesity pharmacotherapies. Specifically, we will use (1) electronic health record (EHR)-enabled clinical discovery to identify phenotypic characteristics predicting weight loss response, (2) genetic risk scores to determine the role genetic susceptibility plays in weight loss response, and (3) pharmacokinetic/pharmacodynamic (PK/PD) modeling to begin identifying individualized dosing regimens of obesity pharmacotherapies in youth with severe obesity. We will be applying these techniques to topiramate, a medication commonly prescribed for weight loss in youth with severe obesity that has been associated with highly variable individual-level effectiveness. This project will generate critical preliminary data to inform the design of an R01 evaluating predictors of response to topiramate and other obesity pharmacotherapies in youth with severe obesity, including agents that are currently (e.g., glucagon-like peptide- 1 (GLP1) receptor agonists) and will be (e.g., glucagon-GLP1 co-agonists, sodium glucose co-transporter 2/1 inhibitors) available in the future. Completing this K23 training program will allow me to establish skills in EHR- enabled clinical discovery and applying genetic risk scores and PK/PD modeling to clinical research that I will need in order to become a leader in the field of precision medicine for obesity management. Further, the multidisciplinary mentorship that I will receive during this career development award will prepare me to become an extramurally funded physician scientist capable of implementing both independent and collaborative large- scale clinical studies.